Cross sectional imaging is an imaging technique which produces a large series of two-dimensional (2D) images of a subject, e.g., a human subject. Examples of cross sectional imaging techniques include computerized tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), SPECT scanning, ultrasonography (US), among others. A set of cross sectional images for a single patient, e.g., for different axially located cross-sections or for the same cross section at different times can be considered three dimensional (3D) image data, and even four dimensional (4D) image data for combinations of axial and temporal cross sectional images.
Various analytical approaches can be applied to the cross sectional images to detect and highlight portions of the patient's anatomy of interest. For example, the cross sectional images can be processed by segmentation, which generally involves separating objects not of interest from objects of interest, e.g., extracting anatomical surfaces, structures, or regions of interest from the images for the purposes of anatomical identification, diagnosis, evaluation, and volumetric measurements. In detecting tumor changes with therapies, volumetric measurement can be more accurate and sensitive than conventional linear measurements. 3D segmentation of cross sectional images provides a feasible way to quantify tumor volume and volume changes over time.
However, segmentation of primary and metastatic tumors, or certain organs (e.g., lungs, liver, spleen, and kidney), which can be highly heterogeneous, is challenging. Furthermore, segmentation of large and complex tumor masses, especially when they are attached to blood vessels and chest walls, as in lung lesions, can be challenging for the techniques currently available.
Identification of lymph nodes from cross sectional images can be important in diagnosing lymphoma. Lymphoma affects about 5% of all cancer cases in the U.S., and the American Cancer Society estimated that 79,190 Americans would be diagnosed with lymphoma in 2012. Enlarged lymph nodes are important indicators of cancer staging, and their size fluctuations can be used for therapy response assessment. The traditional uni-dimensional and bi-dimensional metrics can be based on the assumption that tumors change uniformly in all directions, which is not always the case. Volume as a biomarker can be a better biomarker than a uni-dimensional or bi-dimensional metric.
However, automated segmentation of the lymph node from cross sectional images remains challenging because its boundary can be unclear, its contrast with the background can be weak, and the surrounding structures can be of varying constitutions, e.g., in CT images, high density such as bone, low density such as fat, or similar density such as muscle.
There is a need for accurate and efficient delineation of these objects and measurement of their volumes, e.g., for better therapy response assessment, monitor organ regeneration after transplantation, and make non-invasive diagnoses in both clinical trials and clinical practice.